Holman says the development of
machine learning has come as investors and data scientists employ
less preconceived strategies with
their AI programmes.

“People who did this in the 90shad a propensity to overfit datathat was hard to control,” says Hol-man, who joined from Highbridge,one of the leading quantitativehedge funds in the world. “Becauseof innovations in compute powerand technology this is much morerobust now. You can explore morestrategies and not overfit, which isone of the definitions of successfulinvesting.”Rebellion Research is anoth-er forerunner in this field. Thecompany has been using machinelearning in some form or otherfor the last ten years since it wasfounded by four partners withmathematical but little financialbackground. The company’s ma-chine learning programme looksfor historical macro patterns andconnections and makes investmentdecisions based on this. While notalways correct, it has a 60% accura-cy ratio, says Alexander Fleiss, oneof the four founders.

“The system is dispassionate,”says Fleiss. “It looks for patternsof relationships. We know we’rewrong 30% to 40% of the time butwe are right the rest.”But while sophisticated hedgefunds have been earliest moversin this field, the next few yearscould see the potential benefits ofAI opening up to a bigger market.Quantopian is a Boston-head-

The aim is for AI to one day be available to the masses.

Bloomberg, for one, has been developing AI technologies
for clients to use on its terminal. Applications range
from news text and financial filing analysis to produce
sentiment analysis to a question and answering service
which provides users answers to complicated financial
questions that require the backing of Bloomberg’s data
resources to answer them. Other applications include
predictive analytics - which provide automatic recommendations of events of interest, or content to consume
based on statistical models.

“The interest in AI has changed,” says Gary Kazantsev,
head of the machine learning group at Bloomberg. “Five
to ten years ago people thought this was quite esoteric.
Now everyone around the world wants to have machine
learning incorporated into the business.”

AI for the masses

quartered firm that provides
an online platform for users to
contribute investment algorithms
with the best being allocated
capital, ranging from $100,000 to

$3 million per algorithm. It is, ineffect, a form of crowd-sourcingfor algorithms. The company hasbeen funded by none other thanPoint72 Asset Management run bySteve Cohen, renowned as one ofthe most technology-savvy hedge